Tradeoffs and tools for data quality, privacy, transparency, and trust in citizen science

Abstract

Emerging technologies make it increasingly straightforward for scientists to collect data that are fine in scale, broad in scope, and transparent with open access. However, the resulting datasets may contain sensitive information such as location information about endangered resources or private landowners. These tensions are particularly relevant for citizen science programs which engage the public in answering scientific questions. Citizen science programs are often promoted as being able to achieve multiple scientific (e.g. quality data and open sharing of data) and social goals (e.g. increased transparency and public trust), but likely tensions between these desired outcomes are less frequently discussed. We develop a conceptual framework for tensions in citizen science information and review the internal policies and practices currently used to navigate between data sharing and privacy protection. We also examine the case of Snapshot Wisconsin's wildlife camera traps on private land to understand how program managers balanced data production and sharing with protection of sensitive information and how citizen scientists perceived the project. We found that programs may be forced to make tradeoffs between data quality, privacy protection, resource security, transparency, and trust. In order to maximize conservation outcomes, we recommend that managers anticipate potential tradeoffs in advance of data collection, develop policies and practices to address these, and practice iterative evaluation that solicits feedback from participants.